A low-complexity algorithm for detection of atrial fibrillation using an ECG. (20th June 2018)

• Record Type: Journal Article
• Authors: Sadr, Nadi; Jayawardhana, Madhuka; Pham, Thuy T; Tang, Rui; Balaei, Asghar Tabatabaei; de Chazal, Philip
• Is Part Of: Physiological measurement. Volume 39:Number 6(2018:Jun.)
• Publication Date: 2018-06-20
• Subjects: atrial fibrillation -- arrhythmia -- electrocardiogram -- time domain features -- quadratic neural network -- RR intervals -- linear discriminant analysis; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/aac76c
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: Objectives : We present a method for automatic processing of single-lead electrocardiogram (ECG) with duration of up to 60 s for the detection of atrial fibrillation (AF). The method categorises an ECG recording into one of four categories: normal, AF, other and noisy rhythm. For training the classification model, 8528 scored ECG signals were used; for independent performance assessment, 3658 scored ECG signals. Approach : Our method was based on features derived from RR interbeat intervals. The features included time domain, frequency domain and distribution features. We assessed the performance of three different classifiers (linear and quadratic discriminant analysis, and quadratic neural network (QNN)) on the training set using 100-fold cross-validation. The QNN was selected as the highest performing classifier, and a further performance assessment on the test data made. Main results : On the test set, our method achieved an F1 score for the normal, AF, other and noisy classes of 0.90, 0.75, 0.68 and 0.32, respectively. The overall F1 score was 0.78. Significance : The computational cost of our algorithm is low as all features are derived from RR intervals and are processed by a single hidden layer neural network. This makes it potentially suitable for low-power devices.